Infertility is a common health problem that affects 10-15% of couples of reproductive-age. In the United States alone in the year 2006, approximately 140,000 cycles of in vitro fertilization (IVF) were performed (cdc.gov/art). This resulted in the culture of more than a million embryos annually with variable, and often ill-defined, potential for implantation and development to term. The live birth rate, per cycle, following IVF was just 29%, while on average 30% of live births resulted in multiple gestations (cdc.gov/art). Multiple gestations have well-documented adverse outcomes for both the mother and fetuses, such as miscarriage, pre-term birth, and low birth rate. Potential causes for failure of IVF are diverse; however, since the introduction of IVF in 1978, one of the major challenges has been to identify the embryos that are most suitable for transfer and most likely to result in term pregnancy.
Traditionally in IVF clinics, human embryo viability has been assessed by simple morphologic observations such as the presence of uniformly-sized, mononucleate blastomeres and the degree of cellular fragmentation (Rijinders P M, Jansen C A M. (1998) Hum Reprod 13:2869-73; Milki A A, et al. (2002) Fertil Steril 77:1191-5). More recently, additional methods such as extended culture of embryos (to the blastocyst stage at day 5) and analysis of chromosomal status via preimplantation genetic diagnosis (PGD) have also been used to assess embryo quality (Milki A, et al. (2000) Fertil Steril 73:126-9; Fragouli E, (2009) Fertil Steril June 21 [EPub ahead of print]; El-Toukhy T, et al. (2009) Hum Reprod 6:20; Vanneste E, et al. (2009) Nat Med 15:577-83). However, potential risks of these methods also exist in that they prolong the culture period and disrupt embryo integrity (Manipalviratn S, et al. (2009) Fertil Steril 91:305-15; Mastenbroek S, et al. (2007) N Engl J. Med. 357:9-17).
Recently it has been shown that time-lapse imaging can be a useful tool to observe early embryo development and to correlate early development with potential embryonic viability. Some methods have used time-lapse imaging to monitor human embryo development following intracytoplasmic sperm injection (ICSI) (Nagy et al. (1994) Human Reproduction. 9(9):1743-1748; Payne et al. (1997) Human Reproduction. 12:532-541). Polar body extrusion and pro-nuclear formation were analyzed and correlated with good morphology on day 3. However, no parameters were correlated with blastocyst formation or pregnancy outcomes. Other methods have looked at the onset of first cleavage as an indicator to predict the viability of human embryos (Fenwick, et al. (2002) Human Reproduction, 17:407-412; Lundin, et al. (2001) Human Reproduction 16:2652-2657). However, these methods do not recognize the importance of the duration of cytokinesis or time intervals between early divisions.
Other methods have used time-lapse imaging to measure the timing and extent of cell divisions during early embryo development (WO/2007/144001). However, these methods disclose only a basic and general method for time-lapse imaging of bovine embryos, which are substantially different from human embryos in terms of developmental potential, morphological behavior, molecular and epigenetic programs, and timing and parameters surrounding transfer. For example, bovine embryos take substantially longer to implant compared to human embryos (30 days and 9 days, respectively). (Taft, (2008) Theriogenology 69(1):10-16. Moreover, no specific imaging parameters or time intervals are disclosed that might be predictive of human embryo viability.
While time-lapse imaging has shown promise in the context of automated analysis of early human embryo development, significant development and/or performance hurdles remain unaddressed by these preexisting methods. The nature, timing, and other benchmarks of early human embryo development provide challenges for predicting development behavior. Such challenges can include predicting and/or otherwise determining, via image processing, the number of cell divisions, the timing of cell divisions, and the health of the individual cells and/or zygote at various points during development. Specifically, automated tracking of individual cells, which forms the basis for each of these determinations, can be difficult due to the inherently noisy nature of biological images, as may arise due to lack of distinct visual features, missing and/or false cell boundaries, changing topology of the cell mass due to the cell division/and or cell movement, cell shape deformation, and so on. Any further inference(s) from such automated tracking then can inherit the tracking error(s).
For example, individual cell tracking errors can be further propagated/magnified when the number of cells in each image obtained via automated tracking is the basis for estimating time(s) of cell division event(s). As another example, when the estimated number of cells and/or division timing information is used to determine likelihood of future embryo viability, this automated determination can also be erroneous, and can lead to erroneous decisions, such as whether to proceed with IVF using particular embryos. In addition, when embryos are of similar quality, it can be difficult to differentiate the embryos to determine, for example, which embryos to implant and which embryos to freeze.
It is against this background that a need arose to develop the apparatuses, methods, and systems for image-based human embryo cell classification, image-based embryo outcome determination, and for automated embryo ranking and/or categorization described herein.